streamlit_app.py

import streamlit as st
import pandas as pd
from src.pipelines import top_posts_subreddit_pipeline, comments_pipeline
from src.logger_config import setup_logger
from src.eda import *

logger = setup_logger()

st.set_page_config(layout="wide")


def analyze_sentiment(df, post_id):
    return df[df['title']==post_id]

@st.cache_data(show_spinner = False)
def get_data(subreddit_name, post_limit, comment_limit, post_type):
    return top_posts_subreddit_pipeline(subreddit_name=subreddit_name, post_limit=post_limit, comment_limmit=comment_limit, posts_to_get = post_type)

def main():
    st.title("Reddit Sentiment Analysis")

    if 'df' not in st.session_state:
        st.session_state['df'] = pd.DataFrame()
    if 'subreddit_name' not in st.session_state:
        st.session_state['subreddit_name'] = ''

    with st.sidebar:
        subreddit_name = st.text_input("Enter a subreddit:", st.session_state['subreddit_name'])
        post_type = st.selectbox("Choose post type", ["Top", "Recent"])
        post_limit = st.number_input("Number of top posts to fetch:", min_value=1, max_value=100, value=5, step=1)
        comment_limit = st.number_input("Limit of comments per post:", min_value=1, max_value=500, value=10, step=1)
        search_button_clicked = st.button("Enter")

    # GET data only if subreddit name changes or df is empty
    if search_button_clicked: #and subreddit_name and (subreddit_name != st.session_state['subreddit_name'] or st.session_state['df'].empty):
        with st.spinner(f'Fetching posts for {subreddit_name}...'):
            st.session_state['df'] = get_data(subreddit_name, post_limit, comment_limit, post_type)
        st.session_state['subreddit_name'] = subreddit_name

    st.header(f"Showing posts for: '{subreddit_name}'")
    tab1, tab2 = st.tabs(['Subreddit Analysis', 'Post Analysis'])

    with tab1:    
        st.write("Subreddit Posts:")
        st.dataframe(st.session_state['df'])
        
        if not st.session_state['df'].empty:
            col1, col2= st.columns(2)
            with col1:
                st.header('Sentiment Distribution')
                st.plotly_chart(plot_sentiment_distribution_plotly(st.session_state['df'], 'sentiment_clean_title_label'))

                st.header('Possitive Word Cloud')
                fig = generate_word_cloud_based_on_sentiment(st.session_state['df'], 'clean_title', 'sentiment_clean_title_label', 'pos')
                st.pyplot(fig)

            with col2:
                st.header('Word Count')
                st.plotly_chart(plot_word_count(st.session_state['df'], 'clean_title'))

                st.header('Negative Word Cloud')
                fig = generate_word_cloud_based_on_sentiment(st.session_state['df'], 'clean_title', 'sentiment_clean_title_label', 'neg')
                st.pyplot(fig)
        
            with col1:
                st.header('Sentiment Over Time')
                st.plotly_chart(plot_sentiment_timeseries(st.session_state['df']))
    with tab2:          
        for index, row in st.session_state['df'].iterrows():
            with st.expander(f"Analyze Post: {index} - {row['title']} - {row['sentiment_clean_title_label']}"):
                    
                if st.button(f"Analyze", key=f"{row['title']}+{index}"):
                    with st.spinner(f"Analyzing post {row['title']}..."):
                
                        sentiment = analyze_sentiment(st.session_state['df'], row['title'])
                        logger.info(sentiment.index)
                        comment_df = comments_pipeline(sentiment, 'comments', 'body')
                        st.dataframe(comment_df)


                        if not comment_df.empty:
                            col1, col2= st.columns(2)
                            with col1:
                                st.header('Sentiment Distribution')
                                st.plotly_chart(plot_sentiment_distribution_plotly(comment_df, 'sentiment_clean_body_label'))

                                st.header('Possitive Word Cloud')
                                fig = generate_word_cloud_based_on_sentiment(comment_df, 'clean_body', 'sentiment_clean_body_label', 'pos')
                                st.pyplot(fig)

                            with col2:
                                st.header('Word Count')
                                st.plotly_chart(plot_word_count(comment_df, 'clean_body'))

                                st.header('Negative Word Cloud')
                                fig = generate_word_cloud_based_on_sentiment(comment_df, 'clean_body', 'sentiment_clean_body_label', 'neg')
                                st.pyplot(fig)

                            with col1:
                                st.header('Sentiment Over Time')
                                st.plotly_chart(plot_sentiment_timeseries(comment_df, 'sentiment_clean_body_label'))           
if __name__ == "__main__":
    main()